Machine Learning Approaches based on Wearable Devices for Respiratory Diseases Diagnosis

Paraschiv, Elena-Anca; Rotaru, C.

doi:10.1109/ehb50910.2020.9280098

Cited by 14 publications

(1 citation statement)

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“…On the other hand, the aim of Paraschiv and Rotaru's research [21] was to present some related works that have been made in this field and the proposed method for classifying the International Conference on Biomedical and Health Informatics 2017 (ICBHI'17) scientific challenge respiratory sound database. The method included the extraction of features using Mel-frequency cepstral coefficients (MFCC) and computing a CNN to classify the database.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

Kemper

Oshita²,

Parra³

et al. 2023

IJECE

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<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>

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Section: Introductionmentioning

confidence: 99%